Image processing device

ABSTRACT

An image processing device may create corrected image data by correcting object image data by utilizing base image data. The image processing device may determine a first polar coordinate value which represents first pixels in the object image data, calculate a first orthogonal coordinate by executing an orthogonal transformation on the first polar coordinate value, determine a second polar coordinate value which represents second pixels in the base image data, calculate a second orthogonal coordinate value by executing an orthogonal transformation on the second polar coordinate value, and create the corrected image data by correcting the object image data such that a coordinate value of each particular pixel in the object image data approaches the second orthogonal coordinate value. The each particular pixel may be included in a surrounding area of the first orthogonal coordinate value.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. application Ser.No. 12/647,182 filed on Dec. 24, 2009, which claims priority to JapanesePatent Application No. 2008-327426, filed on Dec. 24, 2008, the contentsof which are hereby incorporated by reference into the presentapplication. This application also claims priority to Japanese PatentApplication No. 2008-327427, filed on Dec. 24, 2008, the contents ofwhich are hereby incorporated by reference into the present application.

TECHNICAL FIELD

The present description discloses image processing technology forcorrecting object image data by utilizing base image data.

DESCRIPTION OF RELATED ART

There is a conventional technology for correcting object image data byutilizing base image data. With this technology, image processingparameters are determined according to the tendency of imagereproduction (for example, hue such as skin color, blue, and green) ofthe base image data, and object image data is corrected based on theimage processing parameters.

SUMMARY

The inventor of this application is engaged in the development of amethod for correcting a color within a particular range of hue of theobject image data so as to approach a color (e.g., blue) within theparticular range of hue of the base image data. The inventor hasdiscovered that in a case where the color in the particular range of hueof the object image data is corrected, the object image data sometimescannot be adequately corrected.

In the present description, a technology is provided that enablesadequate correction of a particular range of hue of object image data byutilizing base image data.

One aspect of techniques disclosed in the present specification is animage processing device for creating corrected image data by correctingobject image data utilizing base image data. The image processing devicemay comprise a first determination unit, a first calculation unit, asecond determination unit, a second calculation unit, and a correctionunit. The first determination unit may be configured to determine afirst polar coordinate value which is a value in a color space of apolar coordinate system. The first polar coordinate value may be arepresentative value which represents first pixels in the object imagedata, and each of the first pixels may have a hue within a particularrange of hue. The first calculation unit may be configured to calculatea first orthogonal coordinate value which is a value in a color space ofa first orthogonal coordinate system by executing an orthogonaltransformation on the first polar coordinate value. The seconddetermination unit may be configured to determine a second polarcoordinate value which is a value in the color space of the polarcoordinate system. The second polar coordinate value may be arepresentative value which represents second pixels in the base imagedata, and each of the second pixels may have a hue within the particularrange of hue. The second calculation unit may be configured to calculatea second orthogonal coordinate value which is a value in the color spaceof the first orthogonal coordinate system by executing an orthogonaltransformation on the second polar coordinate value. The correction unitmay be configured to create the corrected image data by correcting theobject image data such that a coordinate value of each particular pixelin the object image data approaches the second orthogonal coordinatevalue. The each particular pixel may be included in a surrounding areaof the first orthogonal coordinate value in the color space of the firstorthogonal coordinate system.

Another aspect of an image processing device may comprise a firstdetermination unit, a second determination unit, and a correction unit.The first determination unit may be configured to determine a firstrepresentative value which represents first pixels in the object imagedata. Each of the first pixels may have a hue within a particular rangeof hue. The second determination unit may be configured to determine asecond representative value which represents second pixels in the baseimage data. Each of the second pixels may have a hue within theparticular range of hue. The correction unit may be configured to createthe corrected image data by correcting the object image data such that avalue of each particular pixel in the object image data approaches thesecond representative value. The each particular pixel may be includedin a surrounding area of the first representative value. The correctionunit may be configured to correct a value of a correction target pixelsuch that a correction amount becomes greater as a sum of a firstdistance and a second distance becomes smaller. The first distance maybe a distance between the first representative value and the value ofthe correction target pixel, and the second distance may be a distancebetween the second representative value and the value of the correctiontarget pixel.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 shows a schematic configuration of a multi-function device. FIG.2 briefly illustrates how image data is corrected. FIG. 3 shows aflowchart of a correction print process. FIG. 4 shows a flowchart of acorrection process. FIG. 5 shows a continuation of FIG. 4. FIG. 6 showsmathematical formulas for transformation from RGB to HSV. FIG. 7 showsan HSV color space. FIG. 8 shows examples of correspondence relationshipbetween a correction mode and an HSV range. FIG. 9 shows mathematicalformulas for transformation from HSV to vab. FIG. 10 shows mathematicalformulas for calculating a correction amount. FIG. 11 shows an exampleof a graph in which the abscissa corresponds to Pv and the ordinatecorresponds to a sum of Pv and CV. FIG. 12 schematically shows how acoordinate value is corrected. FIG. 13 shows mathematical formulas forcalculating a parameter w. FIG. 14 shows an example of a graph in whichthe abscissa corresponds to Pv and the ordinate corresponds to theparameter w. FIG. 15 shows mathematical formulas for calculating acorrected coordinate value (Mv, Ma, Mb). FIG. 16 schematically shows howwhite color is approached with a higher correction amount in a va plane.FIG. 17 shows mathematical formulas for transformation from vab to HSV.FIG. 18 shows mathematical formulas for transformation from HSV to RGB.FIG. 19 shows an SH plane. FIG. 20 shows an HSV color space. FIG. 21shows a flowchart of a correction process of a second embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT First Embodiment

An embodiment will be explained below with reference to the appendeddrawings. As shown in FIG. 1, a multi-function device 10 has anoperation unit 12, a display unit 14, a slot unit 16, a scanner unit 20,a print unit 22, a network I/F 24, and a control unit 28. The operationunit 12 is constituted by a plurality of keys. A user can input variousinstructions and commands to the multi-function device 10 by operatingthe operation unit 12. The display unit 14 can display various kinds ofinformation. The slot unit 16 has a space for accommodating a memorycard 18. The user can insert the memory card 18 that stores image datainto the slot unit 16. In the present embodiment, the image data storedin the memory card 18 is image data of an RGB format.

The scanner unit 20 has a scan mechanism such as CCD or CIS. The scannerunit 20 can generate image data by color scanning a document, aphotograph, or the like. In the present embodiment, the image datagenerated by the scanner unit 20 is image data of an RGB format. Theprint unit 22 has a print mechanism of an ink jet type, a laser type, orthe like. The print unit 22 can conduct printing on the basis of imagedata stored in the memory card 18 or image data generated by the scannerunit 20. Further, the print unit 22 can conduct printing on the basis ofdata inputted to the network I/F 24. The network I/F 24 is connected toa LAN 26.

The control unit 28 has a CPU 30, a ROM 32, and a RAM 40. The CPU 30executes processing of various types according to programs 34 to 38stored in the ROM 32. The ROM 32 stores various programs 34 to 38. Abasic program 34 is a program for controlling basic operation of themulti-function device 10. The basic program 34 includes e.g., a programfor generating data to be displayed on the display unit 14. Further, thebasic program 34 includes e.g., a program for controlling the scannerunit 20, the print unit 22, and the like. A correction program 36 is aprogram for correcting the image data. The contents of processingexecuted by the CPU 30 according to the correction program 36 will bedescribed below in greater detail. A program 38 is a program other thanthe programs 34 and 36. The RAM 40 can temporarily store the datagenerated in the process in which the CPU 30 executes the processing.

The multi-function device 10 of the present embodiment can correct theimage data stored in the memory card 18 (image data selected by theuser) by using the image data which the scanner unit 20 has generated.More specifically, the multi-function device 10 can correct a colorwithin a hue of the image data stored in the memory card 18 so that theaforesaid color approaches a corresponding color within a hue of theimage data (which is a hue selected by the user) generated by thescanner unit 20.

The image data that is the object of correction (image data stored inthe memory card 18) will be referred to hereinbelow as “RGB object imagedata”. The image data that is used as a reference base for correction(image data generated by the scanner unit 20) will be referred tohereinbelow as “RGB base image data”. The processing will be describedbelow in greater detail. Here, the procedure of correcting the RGBobject image data 50 will be explained in a simple manner using FIG. 2.

The RGB object image data 50 has a plurality of pixels. The value ofeach pixel is specified by a value in a color space (sometimes referredto hereinbelow as “RGB color space”) of a three-dimensional orthogonalcoordinate system. More specifically, the value of each pixel isspecified by a value (0 to 255) indicating R (red), a value (0 to 255)indicating G (green), and a value (0 to 255) indicating B (blue). Themulti-function device 10 transforms the RGB object image data 50 introimage data 52 in an HSV format. The image data 52 will be referred tohereinbelow as “HSV object image data 52”. The multi-functional device10 generates the HSV object image data 52 by transforming the value ofeach pixel (orthogonal coordinate value) constituting the RGB objectimage data 50 into a polar coordinate value. The number of pixels of theHSV object image data 52 is equal to the number of pixels of the RGBobject image data 50. The value of each pixel of the HSV object imagedata 52 is specified by a value in a color space (sometimes referred tohereinbelow as “HSV color space”) of a cylindrical polar coordinatesystem. More specifically, the value of each pixel is specified by avalue (0 to 360°) indicating a hue (H), a value (0 to 1.0) indicating asaturation (S), and a value (0 to 1.0) indicating a brightness (V).Generally, V of HSV means “value”, however “brightness” is utilizedinstead of “value” in the present specification.

Similarly to the above-described procedure of transforming the RGBobject image data 50 into the HSV object image data 52, themulti-function device 10 transforms RGB base image data 60 into imagedata 62 in the HSV format. The image data 62 will be referred tohereinbelow as “HSV base image data 62”. The value of each pixel of theRGB base image data 60 is specified by a value in the RGB color space.Further, the value of each pixel of the HSV base image data 62 isspecified by a value in the HSV color space.

The multi-function device 10 corrects the HSV object image data 52 byutilizing the HSV base image data 62 by executing a variety of thebelow-described processing operations. As a result, corrected image data56 is generated. The image data 56 will be referred to hereinbelow as“corrected RGB image data 56”. The value of each pixel of the correctedRGB image data 56 is specified by a value in the RGB color space.

The contents of a correction print process executed by themulti-function device 10 will be described below in greater detail usingFIG. 3. The user can select a correction print mode by operating theoperation unit 12. In a case where such an operation is performed, thecorrection printing process is executed. The correction print process isexecuted by the CPU 30 according to the basic program 34 and thecorrection program 36. In the flowchart in FIG. 3, a process of S18 isexecuted according to the correction program 36, and the other processesare executed according to the basic program 34.

The user can select one image data from among a plurality of image datastored in the memory card 18 by operating the operation unit 12. Theselected image data becomes the image data that is the object ofcorrection (i.e., the RGB object image data 50). The CPU 30 stands byuntil the RGB object image data 50 is selected (S10).

The user can cause the scanner unit 20 to scan base image (e.g., aphotograph) that is to be used for correcting the RGB object image data50 by operating the operation unit 12. The CPU 30 stands by until thisoperation is executed (S 12). Where this operation is executed by theuser, the CPU 30 causes the scanner unit 20 to conduct scanning (S 14).As a result, the scanner unit 20 color-scans the base image selected bythe user, thereby generating the image data (i.e., the RGB base imagedata 60).

The CPU 30 then displays texts showing a plurality of correction modeson the display unit 14. More specifically, the CPU 30 displays the textsof “Sky Mode”, “Green Mode”, and “Skin Mode” on the display unit 14.Each correction mode will be briefly explained below. The sky mode is amode for correcting the color in a blue hue of the RGB object image data50 so as to approach the color in the blue hue of the RGB base imagedata 60. The green mode is a mode for correcting the color in a greenhue of the RGB object image data 50 so as to approach the color in thegreen hue of the RGB base image data 60. The skin mode is a mode forcorrecting the color in a skin hue of the RGB object image data 50 so asto approach the color in the skin hue of the RGB base image data 60. Theuser can select one correction mode from the three correction modesdisplayed on the display unit 14 by operating the operation unit 12. TheCPU 30 stands by until this operation is executed (S 16). Informationindicating the correction mode selected by the user is stored in the RAM40.

The CPU 30 then executes a correction process (S 18). The contents ofthe correction process will be described below in greater detail. Thecorrected RGB image data 56 is obtained by the correction process. TheCPU 30 converts the corrected RGB image data 56 into printing data(CMYK-format bitmap data) (S20). Then, the CPU 30 causes the print unit22 to conduct printing based on the printing data generated in S20(S22). As a result, the print unit 22 conducts printing on a printingmedium on the basis of the printing data. The user can thus obtain theprinting medium with an image corresponding to the corrected RGB imagedata 56.

The contents of the correction process executed in S18 will be explainedbelow in greater detail with reference to FIG. 4 and FIG. 5. The CPU 30transforms the RGB object image data 50 into the HSV object image data52 (S40). FIG. 6 shows mathematical formulas for transforming acoordinate value in the RGB color space into a coordinate value in theHSV color space. The CPU 30 selects one pixel (transformation targetpixel) constituting the RGB object image data 50. As shown in FIG. 6(a), a coordinate value of the transformation target pixel is specifiedas (R, G, B), and a coordinate value in the HSV color space that isobtained by transformation from the coordinate value of thetransformation target pixel is specified as (H, S, V). As shown in FIG.6( b), the CPU 30 calculates R/255, G/255, and B/255 and specifies thelargest value among them as V. Further, the CPU 30 calculates S by themathematical formulas shown in FIG. 6( c). The CPU 30 then calculatesintervening variables r, g, b by the mathematical formulas shown in FIG.6( d). The CPU 30 calculates H by the mathematical formulas shown inFIG. 6( e). Where the H calculated herein has a negative value, the CPU30 calculates a new H by adding 360 to the H having the negative value,as shown in FIG. 6( f). As a result, the coordinate value (H, S, V) inthe HSV color space can be obtained from the coordinate value (R, G, B)of the transformation target pixel.

As clearly shown in FIG. 7, the HSV color space is a color space in acylindrical coordinate system. Here, H (deviation angle) indicating hueis any value from 0 to 360°, and S (radius vector) indicating saturationis any value from 0 to 1.0. Further, V indicating brightness is anyvalue from 0 to 1.0.

The CPU 30 calculates, for each of a rest of pixels of the RGB objectimage data 50, a coordinate value (H, S, V) in the HSV color space bythe mathematical formulas shown in FIG. 6( a) to FIG. 6( f). As aresult, the HSV object image data 52 is obtained. The CPU 30 stores theHSV object image data 52 in the RAM 40.

The CPU 30 then transforms the RGB base image data 60 into the HSV baseimage data 62 (S42). The processing of S42 is conducted by a methodsimilar to that of the processing of the above-described S40. That is,the CPU 30 calculates, for each of all pixels of the RGB base image data60, a coordinate value (H, S, V) in the HSV color space by themathematical formulas shown in FIG. 6( a) to FIG. 6( f). As a result,the HSV base image data 62 is obtained. The CPU 30 stores the HSV baseimage data 62 in the RAM 40.

The CPU 30 then calculates a representative value from the HSV objectimage data 52 (S44). This representative value will be referred tohereinbelow as “object representative value (OH, OS, OV)”. Theprocessing of S44 is executed according to the correction mode selectedin S16 shown in FIG. 3. FIG. 8 shows an example of a correspondencerelationship between the correction mode and ranges of H, S, and V. Forexample, in a case where the correction mode is the sky mode, H is 180°to 240°, S is 0 to 1.0, and V is 0 to 1.0. It means that in a case wherethe correction mode is the sky mode, the CPU 30 calculates therepresentative value based on pixels having H of 180° to 240°, S of 0 to1.0, and V of 0 to 1.0.

More specifically, in a case where the correction mode is the sky mode,the CPU 30 specifies pixels having H of 180° to 240° from among theentire pixels constituting the HSV object image data 52. Here, S and Vare not taken into account. This is because the ranges of S and V in thecase of the sky mode are both 0 to 1.0, and the S and V are contained inthe 0 to 1.0 range for each pixel. Then, the CPU 30 specifies OH bycalculating an average value of H of the specified pixels, specifies OSby calculating an average value of S of the specified pixels, andspecifies OV by calculating an average value of V of the specifiedpixels. The object representative value (OH, OS, OV) is therebyobtained. Similarly, in a case where the correction mode selected in S16of FIG. 3 is the green mode, the CPU 30 specifies the objectrepresentative value (OH, OS, OV) from pixels having H of 50° to 170°(see FIG. 8( b)). In a case where the correction mode selected in S16 ofFIG. 3 is the skin mode, the CPU 30 specifies the object representativevalue (OH, OS, OV) from pixels having H of 10° to 40°, S of 0.1 to 0.6,and V of 0.2 to 1.0 (see FIG. 8( c)).

Next, the CPU 30 calculates a representative value from the HSV baseimage data 62 (S46). This representative value will be referred tohereinbelow as a base representative value (SH, SS, SV). The processingof S46 is executed similarly to the processing of S44. For example, in acase where the correction mode selected in S16 of FIG. 3 is the skymode, the CPU 30 specifies pixels having H of 180° to 240° (see FIG. 8(a) from among the entire pixels constituting the HSV base image data 62.Then the CPU 30 specifies SH by calculating the average value of H ofthe specified pixels, specifies SS by calculating the average value of Sof the specified pixels, and specifies SV by calculating the averagevalue of V of the specified pixels. As a result, the base representativevalue (SH, SS, SV) is obtained.

The CPU 30 then transforms the object representative value (OH, OS, OV)specified in S44 into a coordinate value in a vab color space (S48).This coordinate value will be referred to hereinbelow as “objectcoordinate value (Ov, Oa, Ob)”. The vab color space is a color space ofa three-dimensional orthogonal coordinate system that is different fromthe RGB color space. The vab color space is a color space specific tothe present embodiment and is not the conventional color space. Thecontents of the processing of S48 will be explained below with referenceto FIG. 9. FIG. 9 shows mathematical formulas for transforming acoordinate value in the HSV color space into a coordinate value in thevab color space. In FIG. 9, a coordinate value of a transformationtarget pixel is specified as (H, S, V), and a coordinate value in thevab color space that is obtained by transformation from thetransformation target value is specified as (v, a, b). Where (H, S, V)is substituted with (OH, OS, OV) and (v, a, b) is substituted with (Ov,Oa, Ob) in the mathematical formula shown in FIG. 9, mathematicalformulas for obtaining the object coordinate value (Ov, Oa, Ob) can beobtained. The CPU 30 calculates the object coordinate value (Ov, Oa, Ob)by these mathematical formulas.

The CPU 30 then transforms the base representative value (SH, SS, SV)specified in S46 into a coordinate value in the vab color space (S50).This coordinate value will be referred to hereinbelow as “basecoordinate value (Sv, Sa, Sb)”. The processing of S50 is executedsimilarly to the processing of S48. Where (H, S, V) is substituted with(SH, SS, SV) and (v, a, b) is substituted with (Sv, Sa, Sb) in themathematical formulas shown in FIG. 9, mathematical formulas forobtaining the base coordinate value (Sv, Sa, Sb) can be obtained. TheCPU 30 calculates the base coordinate value (Sv, Sa, Sb) by thesemathematical formulas.

The CPU 30 then selects one pixel constituting the HSV object image data52 (S52). The coordinate value of the pixel selected in S52 is specifiedby (PH, PS, PV) (can also be alternatively called hereinbelow as “pixelvalue”). The CPU 30 transforms the coordinate value (PH, PS, PV) of thepixel selected in S52 into a coordinate value (Pv, Pa, Pb) in the vabcolor space (S54). The processing of S54 is executed similarly to theprocessing of S48. Where (H, S, V) is substituted with (PH, PS, PV) and(v, a, b) is substituted with (Pv, Pa, Pb) in the mathematical formulasshown in FIG. 9, mathematical formulas for obtaining the coordinatevalue (Pv, Pa, Pb) can be obtained. The CPU 30 calculates the coordinatevalue (Pv, Pa, Pb) by these mathematical formulas.

The CPU 30 then calculates a correction amount (Cv, Ca, Cb) for thecoordinate value (Pv, Pa, Pb) (S56). FIG. 10 shows mathematical formulasfor calculating the correction amount. The CPU 30 calculates Cv on thebasis of the Pv calculated in S54, Ov calculated in S48, and Svcalculated in S50. First, the CPU 30 calculates a lower limit value Lvand an upper limit value Uv by the mathematical formulas shown in FIG.10( a). Then, the CPU 30 calculates Curvy by the mathematical formulasshown in FIG. 10( b). AdjS and AdjO contained in the mathematicalformulas shown in FIG. 10( b) are predetermined constants, rather thannumerical values depending on Pv, Ov, and Sv. The CPU 30 then calculatesCv by the mathematical formulas shown in FIG. 10( c).

FIG. 11 shows a graph in which the abscissa corresponds to Pv and theordinate corresponds to a sum of Pv and Cv (referred to hereinbelow as“Mv”). In the graph shown in FIG. 11, a case in which Ov is 0.25 and Svis 0.38 is presented by way of example. Mv is a corrected value which isobtained by correcting Pv by Cv. In the present embodiment, Mv is notobtained by merely adding Cv to Pv, as explained below in greaterdetail. However, in order to facilitate easier understanding of Cv, asum of Pv and Cv is taken as Mv in FIG. 11.

As follows from the graph shown in FIG. 11, when Pv has a value betweenthe lower limit value Lv and the upper limit value Uv, Pv follows acorrection curve that approaches Sv. This correction curve is dividedinto an area that is convex upward and an area that is convex downward,with Sv as a boundary. For example, where Pv is equal to Ov, Cv becomesC1. Mv in this case is V1 that is a sum of Ov and C1. V1 has a valuebetween Ov and Sv. For example, when Pv is a value V2 between Ov and Sv,Cv becomes C2. Mv in this case is V3 that is a sum of V2 and C2. V3 is avalue between V2 and Sv. More specifically, V3 is a value between V1 andSv. Thus, closer the value Pv before the correction to Sv, closer thevalue Mv after the correction to Sv. How the correction is performed toV1 when Pv is Ov and to V3 when Pv is V2 is also shown in FIG. 12( a).

The CPU 30 calculates the correction amount Ca by using a proceduresimilar to that used to calculate the correction amount Cv. The CPU 30calculates Ca on the basis of Pa, Oa, and Sa. Ca is calculated by themathematical formulas similar to those shown in FIG. 10. Morespecifically, where Lv is changed to La, Uv is changed to Ua, Ov ischanged to Oa, Sv is changed to Sa, and Curvy is changed to Curva inFIG. 10( a) and FIG. 10( b), Curva can be calculated. The CPU 30calculates Ca by the mathematical formulas shown in FIG. 10( c).

Where a graph has Pa plotted relative to the abscissa and a sum of Paand Ca (referred to herein as Ma) plotted relative to the ordinate, agraph similar to that in FIG. 11 can be obtained. Thus, when Pa has avalue between the lower limit value La and the upper limit value Ua, Pais corrected so as to approach Sa. FIG. 12( b) shows an example of Pacorrection. Where Pa is equal to Oa, Pa is corrected to a value A1between Oa and Sa. Where Pa has a value A2 between Oa and Sa, Pa iscorrected to a value A3 between A1 and Sa. Thus, closer the value of Pabefore the correction to Sa, closer the value after the correction toSa.

The CPU 30 then calculates the correction amount Cb by using a proceduresimilar to that used for calculating the correction amount Cv. The CPU30 calculates Cb on the basis of Pb, Ob, and Sb. Where Lv is changed toLb, Uv is changed to Ub, Ov is changed to Ob, Sv is changed to Sb, andCurvy is changed to Curvb in FIG. 10( a) and FIG. 10( b), Curvb can becalculated. The CPU 30 calculates Cb by the mathematical formulas shownin FIG. 10( c).

Where a graph has Pb plotted relative to the abscissa and a sum of Pband Cb (referred to herein as Mb) plotted relative to the ordinate, agraph similar to that in FIG. 11 can be obtained. Thus, when Pb has avalue between the lower limit value Lb and the upper limit value Ub, Pbis corrected so as to approach Sb. FIG. 12( c) shows an example of howPb is corrected. Where Pb is equal to Ob, Pb is corrected to a value B1between Ob and Sb. Where Pb has a value B2 between Ob and Sb, Pb iscorrected to a value B3 between B1 and Sb. Thus, closer the value of Pbbefore the correction to Sb, closer the value after the correction toSb.

As follows from FIG. 12, in a case where a coordinate value (Pv, Pa, Pb)of a correction target pixel (pixel selected in S52 shown in FIG. 4) isequal to the object coordinate value (Ov, Oa, Ob), the coordinate value(Pv, Pa, Pb) is corrected to a coordinate value (V1, A1, B1) that isbetween the object coordinate value (Ov, Oa, Ob) and base coordinatevalue (Sv, Sa, Sb). In a case where the coordinate value (Pv, Pa, Pb) isa coordinate value (V2, A2, B2) between the object coordinate value (Ov,Oa, Ob) and base coordinate value (Sv, Sa, Sb), the coordinate value(V2, A2, B2) is corrected to a coordinate value (V3, A3, B3) that isbetween the coordinate value (V1, A1, B1) and base coordinate value (Sv,Sa, Sb).

Once the correction amount (Cv, Ca, Cb) is calculated, the CPU 30advances to S58 shown in FIG. 5. In S58, the CPU 30 calculates aparameter “d” and a parameter “range”. FIG. 13( a) shows mathematicalformulas for calculating the parameter “d” and the parameter “range”.The CPU 30 calculates the parameter “d” and the parameter “range” by themathematical formula shown in FIG. 13( a). The parameter “d” is a sumtotal of a distance between the coordinate value (Pv, Pa, Pb) of thecorrection target pixel and the object coordinate value (Ov, Oa, Ob),and a distance between the coordinate value (Pv, Pa, Pb) of thecorrection target pixel and the base coordinate value (Sv, Sa, Sb). Theparameter “range” is a distance between the object coordinate value (Ov,Oa, Ob) and the base coordinate value (Sv, Sa, Sb).

The CPU 30 then calculates a parameter “w” on the basis of the parameter“d” and the parameter “range” (S60). FIG. 13( b) shows mathematicalformulas for calculating “w”. A parameter “sui” in the mathematicalformulas shown in FIG. 13( b) is set correspondingly to the correctionmode selected in S16 shown in FIG. 3. FIG. 13( c) shows a correspondencerelationship between a correction mode and “sui”. A “sui” in a case ofthe green mode is the largest, and a “sui” in a case of the skin mode isthe smallest. A “sui” in a case of the sky mode is between the “sui” forthe green mode and the “sui” for the skin mode. As follows from FIG. 13(b), where “d” is equal to “range”, “w” assumes a maximum value of 1.0.Where “d” is larger than “range”, “w” decreases with the increase in“d”. Where “d” is larger than a value obtained by multiplying “range”and “sui”, “w” assumes the smallest value of 0. According to themathematical formulas shown in FIG. 13( b), it can be said that “w”decreases with the increase in “d”. In other words, “w” increases withthe decrease in “d”.

FIG. 14 shows a graph in which the abscissa corresponds to Pv and theordinate corresponds to “w”. The graph in FIG. 14 illustrates by way ofexample a case in which Ov is 0.2 and Sv is 0.31. The graph in FIG. 14clearly shows that when Pv is a value between Ov and Sv (i.e., when“d”=“range”), “w” is 1.0. As Pv becomes less than Ov, “w” decreasessmoothly. Further, as Pv becomes larger than Sv, “w” decreases smoothly.As Pv becomes farther from both Ov and Sv, “w” decreases. Conversely, asPv comes closer to both Ov and Sv, “w” increases. A graph in which theabscissa corresponds to Pa and the ordinate corresponds to “w”, and agraph in which the abscissa corresponds to Pv and the ordinatecorresponds to “w” are similar to the graph shown in FIG. 14.

The CPU 30 corrects the coordinate value (Pv, Pa, Pb) of the correctiontarget pixel on the basis of the parameter “w” and the correction amount(Cv, Ca, Cb) (S62). As a result, the corrected coordinate value (Mv, Ma,Mb) is obtained. FIG. 15 shows mathematical formulas for calculating thecorrected coordinate value (Mv, Ma, Mb). The CPU 30 calculates thecorrected coordinate value (Mv, Ma, Mb), by adding a coordinate valueobtained by multiplying the correction amount (Cv, Ca, Cb) by theparameter “w”, to the coordinate value (Pv, Pa, Pb) of the correctiontarget pixel.

As described hereinabove, the actual correction amount is calculated bymultiplying the correction amount (Cv, Ca, Cb) by the parameter “w”. Theparameter “w” is calculated by the mathematical formulas shown in FIG.13( b), and increases in accordance with the decrease in “d”. Thesmaller is “d”, the larger is the actual correction amount (w×(Cv, Ca,Cb)). Thus, the smaller is “d”, the larger is the correction of thecoordinate value (Pv, Pa, Pb) of the correction target pixel. FIG. 16shows an object coordinate value O and a base coordinate value S on theva plane. FIG. 16 means that the closer is the color to white, thelarger will be the correction. Conversely speaking, FIG. 16 means thatthe closer is the color to black, the smaller will be the correction. Aperfectly black area means that no correction is performed thereon. FIG.16 also demonstrates that the smaller is the above-described parameter“d”, the larger is the correction of the coordinate value (Pv, Pa, Pb)of the correction target pixel.

The perfectly black area in FIG. 16 is an area outside an ellipse havingthe object coordinate value O and base coordinate value S as two focus.Thus, in a case where the coordinate value (Pv, Pa, Pb) of thecorrection target pixel is present in the area outside the aforesaidellipse, this coordinate value is not corrected. Conversely speaking, ina case where the coordinate value (Pv, Pa, Pb) of the correction targetpixel is present in the area inside the ellipse, this coordinate valueis corrected. FIG. 16 uses representation in the va plane, but similardrawings are also obtained with representation in the vb plane and abplane. Thus, in a case where the coordinate value (Pv, Pa, Pb) of thecorrection target pixel is present within the area outside the ellipsoidin the vab space, this coordinate value is not corrected. Converselyspeaking, in a case where the coordinate value (Pv, Pa, Pb) of thecorrection target pixel is present in the area inside the ellipsoid,this coordinate value is corrected.

Having calculated the corrected coordinate value (Mv, Ma, Mb) in S62,the CPU 30 advances to S64. In S64, the CPU 30 transforms the correctedcoordinate value (Mv, Ma, Mb) into a coordinate value (MH, MS, MV) inthe HSV color space. FIG. 17 shows mathematical formulas fortransforming a coordinate value in the vab color space into a coordinatevalue in the HSV color space. In FIG. 17, a coordinate value of atransformation target pixel is specified by (v, a, b), and a coordinatevalue in the HSV color space that is obtained by transformation from thecoordinate value of the transformation target pixel is specified by (H,S, V). Where (v, a, b) in the mathematical formulas shown in FIG. 17 isreplaced with (Mv, Ma, Mb) and (H, S, V) is replaced with (MH, MS, MV),mathematical formulas for obtaining a coordinate value (MH, MS, MV) areobtained. The CPU 30 calculates the coordinate value (MH, MS, MV) bythese mathematical formulas.

The CPU 30 then transforms the coordinate value (MH, MS, MV) specifiedin S64 into a coordinate value (MR, MG, MB) in the RGB color space(S66). FIG. 18 shows mathematical formulas for transforming a coordinatevalue in the HSV color space into a coordinate value in the RGB colorspace. In FIG. 18, a coordinate value of a transformation target pixelis specified by (H, S, V), and a coordinate value in the RGB color spacethat is obtained by transformation from the coordinate value of thetransformation target pixel is specified as (R, G, B). Where (H, S, V)in the mathematical formulas shown in FIG. 18 is replaced with (MH, MS,MV) and (R, G, B) is replaced with (MR, MG, MB), mathematical formulasfor obtaining a coordinate value (MR, MG, MB) are obtained. As shown inFIG. 18( a), the CPU 30 calculates intervening variables “in” and “fl”from MH. Further, as shown in FIG. 18( b), where the interveningvariable “in” is an even number, the CPU 30 subtracts “fl” from 1 (one),and calculates new “fl”. Then, the CPU 30 calculates interveningvariables “m” and “n” from MS and MV by the mathematical formulas shownin FIG. 18( c). The CPU 30 calculates MR, MG, and MB from MV, “m”, and“n” by the mathematical formulas shown in FIG. 18( d). As a result, thecoordinate value (MR, MG, MB) in the RGB color space is obtained.

By executing the processes of S54 to S66, it is possible to correct thecoordinate value (PH, PS, PV) of one pixel in the HSV object image data52 and obtain the coordinate value (MR, MG, MB) in the RGB color spaceas the corrected coordinate value. The CPU 30 determines whether theprocessing of S54 to S66 has been executed with respect to all thepixels in the HSV object image data 52 (S68). Where the result is NO,the CPU 30 selects the next pixel in the HSV object image data 52 (S70).The CPU 30 executes the process of S54 to S66 with respect to the pixelselected in S70. Where the result of determination in S68 is YES, thecorrection process is completed. In this case, the corrected RGB imagedata 56 (see FIG. 2) in which the value of each pixel is specified bythe coordinate value in the RGB color space obtained by processing ofS54 to S66 is obtained.

The multi-function device 10 of the present embodiment is explainedabove in detail. The multi-function device 10 executes the processing inS44 and S46 of FIG. 4 in the HSV color space (see FIG. 7). For example,in S44 of FIG. 4, the multi-function device 10 specifies a group ofpixels having a hue H (for example, 180° to 240° in the case of skymode) corresponding to the correction mode desired by the user from theHSV object image data 52, and calculates the object representative valuefrom this pixel group.

Since the object representative value and the base representative valueare calculated in the HSV color space, employing a method of correctingeach pixel of the HSV object image data 52 in the HSV color space can bealso considered. However, in this case, the following problems areencountered. In FIG. 19, α shows a range of hue H (for example, 180° to240° in the case of sky mode) corresponding to the correction mode.Where the method for correcting in the HSV color space is used, when twopixels 120 and 124 with small saturations S are present in positionsshown in FIG. 19, the pixel 120 is corrected, but the pixel 124 is notcorrected. As a result, the two pixels 120 and 124 that practically didnot differ in visible color prior to the correction differ significantlyin color after the correction. In order to resolve this problem in themethod of correcting in the HSV color space, it is necessary to correctthe group of pixels contained in the area e.g., shown by hatching inFIG. 20. However, in order to define the area including the V axis inthe HSV color space, it is necessary to define S and V for each ofvarious H values, thus a huge amount of calculations is required.

By contrast, the multi-function device 10 of the present embodimentexecutes the correction in the vab color space of an orthogonalcoordinate system. That is, the multi-function device 10 transforms theobject representative value (OH, OS, OV) into the object coordinatevalue (Ov, Oa, Ob) in the vab color space (S48 in FIG. 4), and correctsthe values of pixels contained in an area (area inside the ellipsoidhaving the object coordinate value (Ov, Oa, Ob) and base coordinatevalue (Sv, Sa, Sb) as two focuses) surrounding the object coordinatevalue (Ov, Oa, Ob). As a result, even when the pixel 124 is present thatdoes not have a hue within a range of hue H corresponding to thecorrection mode, the value of this pixel 124 may be corrected. This isbecause there is a high possibility that a group of pixels (e.g., thepixels 122, 124) having an almost achromatic color (having extremelysmall saturation) is disposed adjacently each other in an orthogonalcoordinate system (in this embodiment, the vab color space). Thus, it ispossible to inhibit occurrence of a significant difference inpost-correction colors between the two pixels 120 and 124 thatpractically did not have much difference in visible colors before thecorrection. The multi-function device 10 of the present embodiment canadequately correct the HSV object image data 52 by using the HSV baseimage data 62.

As shown by the mathematical formulas in FIG. 13, the multi-functiondevice 10 has been set such that a value range (“range”×“sui”) of apixel that has to be corrected increases in accordance with the increasein the value of “sui” corresponding to the correction mode. The “sui” isthe largest in the green mode, and is the smallest in the skin mode.Therefore, in a case where green color for which color variations aredifficult to recognize is corrected, the value range of the pixel thathas to be corrected increases, and in a case where skin color for whichcolor variations are easy to recognize is corrected, the value range ofthe pixel that has to be corrected decreases. The value range of thepixel that has to be corrected is adequately set with consideration forhuman vision characteristic.

The correction curve shown in FIG. 11 illustrates an example in which“w” is 1.0. In the example shown in FIG. 11, V4 is not corrected, and V5is corrected into V6. Because values of V4 and V5 are close to eachother, the visible colors are similar. In a case where a pixelcorresponding to V4 and a pixel corresponding to V5 are adjacent in theHSV object image data 52, the former pixel that is not corrected (thatis, V=V4) and the latter pixel that is corrected (that is, V=V6) mighthave a significant difference in color. In the present embodiment, thefollowing configuration is used to prevent the occurrence for such anevent. Thus, the parameter “w” is used such that the correction amountincreases according to a degree of decrease in a sum “d” of a distancebetween the object coordinate value (Ov, Oa, Ob) and the coordinatevalue (Pv, Pa, Pb) of the correction target pixel, and a distancebetween the base coordinate value (Sv, Sa, Sb) and the coordinate value(Pv, Pa, Pb) of the correction target pixel. The parameter “w” becomessmaller in accordance with the increase in “d” (see FIG. 13( b)). Apixel corresponding to V5 shown in FIG. 11 is present in a location thatis far from both the object coordinate value (Ov in FIG. 11) and thebase coordinate value (Sv in FIG. 11). Therefore, the correction amountfor the pixel corresponding to V5 is small. As a result, even in a casewhere the pixel corresponding to V4 and the pixel corresponding to V5are adjacent, occurrence of a large difference in color between thesepixels can be prevented.

The multi-function device 10 is an example of “an image processingdevice”. The RGB object image data 50, HSV object image data 52, HSVbase image data 62, and corrected RGB image data 56 are the examples of“particular image data”, “object image data”, “base image data”, and“corrected image data”, respectively. The range of hue H (see FIG. 8)determined correspondingly to each correction mode is an example of “aparticular range of hue”. The HSV color space, vab color space, and RGBcolor space are the examples of “a color space of a polar coordinatesystem”, “a color space of a first orthogonal coordinate system (anorthogonal coordinate system)”, and “a second orthogonal coordinatesystem”, respectively. The object representative value (OH, OS, OV) andobject coordinate value (Ov, Oa, Ob) are the examples of “a first polarcoordinate value” and “a first orthogonal coordinate value (a firstrepresentative value)”, respectively. The base representative value (SH,SS, SV) and base coordinate value (Sv, Sa, Sb) are the examples of “asecond polar coordinate value” and “a second orthogonal coordinate value(a second representative value)”, respectively. The processes of S44 andS48 in FIG. 4 are examples of processes executed by “a firstdetermination unit” and “a first calculation unit”, respectively. Theprocesses of S46 and S50 in FIG. 4 are examples of processes executed by“a second determination unit” and “a second calculation unit”,respectively. Further, the processes of S52 to S56 in FIG. 4 and S58 toS70 in FIG. 5 are examples of processes executed by “a correction unit”.The area of w>0 (area with a color other than black in FIG. 16)determined by the mathematical formulas shown in FIG. 13 is an exampleof “a surrounding area” and “a particular area”.

The parameter “d” is an example of “a sum of a first distance and asecond distance”. (V1, A1, B1), (V2, A2, B2), (V3, A3, B3) in FIG. 12are the examples of “a third orthogonal coordinate value (a firstcoordinate value)”, “a fourth orthogonal coordinate value (a secondcoordinate value)”, and “a fifth orthogonal coordinate value (a thirdcoordinate value)”, respectively. The process of S40 in FIG. 4 is anexample of a process executed by “a transformation unit”. Further, thecoordinate value (Pv, Pa, Pb) and coordinate value (MH, MS, MV) are theexamples of “a particular orthogonal coordinate value” and “a particularpolar coordinate value”, respectively.

Second Embodiment

The second embodiment will be described below. In the presentembodiment, the contents of the correction process of S18 in FIG. 3 aredifferent from those of the first embodiment. In the first embodiment,the coordinate value of the correction target pixel is corrected in thevab color space. By contrast, in the present embodiment, the coordinatevalue of the correction target pixel is corrected in the RGB colorspace.

FIG. 21 is a flowchart of a correction process of the presentembodiment. By executing the processes of S40 to S46 in FIG. 4, the CPU30 calculates the object representative value (OH, OS, OV) and alsocalculates the base representative value (SH, SS, SV). Then, the CPU 30transforms the object representative value (OH, OS, OV) into an objectcoordinate value (OR, OG, OB) in the RGB color space (S148). Further,the CPU 30 transforms the base representative value (SH, SS, SV) into anobject coordinate value (SR, SG, SB) in the RGB color space (S 150). Theprocesses of S148 and S150 are executed by the mathematical formulasshown in FIG. 18.

Then, the CPU 30 selects one pixel (correction target pixel)constituting the RGB object pixel data 50 (S 152). The coordinate valueof the pixel selected in S152 will be represented hereinbelow as (PR,PG, PB). The CPU 30 calculates a correction amount (CR, CG, CB) of thecoordinate value (PR, PG, PB) of the correction target pixel selected inS 152. The correction amount is calculated by the mathematical formulasshown in FIG. 10. More specifically, CR can be calculated by changing Lvto LR, changing Uv to UR, changing Ov to OR, changing Sv to SR, changingPv to PR, changing Curvy to CurvR, and Cv to CR in FIG. 10. In thepresent embodiment, the description of “Lv=1.0 when Lv>1.0” in FIG. 10(a) is changed to “LR=255 when LR>255”. Further, the description of“Uv=1.0 when Uv>1.0” in FIG. 10( a) is changed to “UR=255 when UR>255”.A method for calculating CG and CB is similar to that for calculatingCR.

The CPU 30 then calculates a parameter “d” and a parameter “range” (S158). The parameter “d” and the parameter “range” are calculated by themathematical formulas shown in FIG. 13( a). The parameter “d” is a sumof a distance between the coordinate value (PR, PG, PB) of thecorrection target pixel and the object coordinate value (OR, OG, OB) anda distance between the coordinate value (PR, PG, PB) of the correctiontarget pixel and the base coordinate value (SR, SG, SB). The parameter“range” is a distance between the object coordinate value (OR, OG, OB)and the base coordinate value (SR, SG, SB).

The CPU 30 then calculates a parameter “w” on the basis of the parameter“d” and the parameter “range” (S 160). The parameter “w” is calculatedby the mathematical formulas shown in FIG. 13( b). The CPU 30 thencorrects the coordinate value (PR, PG, PB) of the correction targetpixel on the basis of the parameter “w” and the correction amount (CR,CG, CB) (S 162). As a result, the corrected coordinate value (MR, MG,MB) is obtained. In the processing of S 162, calculations are conductedby the mathematical formulas shown in FIG. 15. Thus, the CPU 30calculates the corrected coordinate value (MR, MG, MB) by adding acoordinate value obtained by multiplying the correction amount (CR, CG,CB) by the parameter “w” to the coordinate value (PR, PG, PB) of thecorrection target pixel.

Once the corrected coordinate value (MR, MG, MB) has been calculated inS 162, the CPU 30 advances to S168. In S168, the CPU 30 determineswhether the processes of S156 to S162 have been executed with respect toall the pixels in the RGB object image data 50. Where the result is NO,the CPU 30 selects the next pixel in the RGB object image data 50 (S170) and executes the processes of S156 to S 162. Where the result ofdetermination in S168 is YES, the correction process is completed. Inthis case, the corrected RGB image data 56 (see FIG. 2) is obtained.

In the first embodiment, it is necessary to conduct a process oftransforming the coordinate value (Mv, Ma, Mb) into the coordinate value(MH, MS, MV) in the HSV color space (S64) and transform the coordinatevalue (MH, MS, MV) into the coordinate value (MR, MG, MB) in the RGBcolor space (S66) after the corrected coordinate value (Mv, Ma, Mb) hasbeen calculated in S62 of FIG. 5. By contrast, in the presentembodiment, the transformation process is unnecessary because thecoordinate value (MR, MG, MB) obtained by the process of S162 in FIG. 21is a coordinate value in the RGB color space.

The above-described embodiments are illustrative examples, and theseembodiments can be changed in a variety of ways. The variation examplesof the embodiments are described below.

-   -   (1) In the first embodiment, in a case where the parameter “w”        calculated in S60 in FIG. 5 is 0, no correction is performed        with respect to the pixel selected in S52 in FIG. 4. Therefore,        in a case where the parameter “w” is 0, S62 and S64 in FIG. 5        may be skipped. In this case, the CPU 30 may execute process of        S66 in FIG. 5 by taking the coordinate value (PH, PS, PV) of the        pixel selected in S52 in FIG. 4 as the coordinate value (MH, MS,        MV). Here, if the coordinate value corresponding to the        coordinate value (PH, PS, PV) in the RGB object image data 50        has been already clarified, S66 may be skipped.    -   (2) In the above-described embodiments, image data stored in the        memory card 18 is taken as the RGB object image data 50 that is        the object of correction. However, other image data may be also        taken as the RGB object image data 50. For example, image data        generated by the scanner unit 20 may be also taken as the RGB        object image data 50. Further, in the above-described        embodiments, image data generated by the scanner unit 20 is        taken as the RGB base image data 60. However, other image data        may be also taken as the RGB base image data 60. For example,        image data stored in the memory card 18 may be taken as the RGB        base image data 60.    -   (3) In the above-described embodiments, a color space in a        cylindrical coordinate system (HSV color space) is used as the        color space of the polar coordinate system. However, a color        space of a spherical coordinate system may be also used as the        color space of the polar coordinate system.    -   (4) In the above-described embodiments, a range of hue that has        to be corrected is determined correspondingly to the correction        mode selected by the user. However, in the correction print        process, only one correction mode may be present (that is, the        range of hue that has to be corrected may be fixed). Further, in        the correction print process, the correction process may be        successively executed with respect to each of a plurality of        correction modes (such as the three correction modes in the        above-described embodiments). In this case, the multi-function        device 10 may display a plurality of image data after the        correction, may allow the user to select one image data from        among these image data, and may execute printing on the basis of        the image data selected by the user.    -   (5) In each of the above-described embodiments, the correction        is not necessarily conducted by using “w”. Thus, in the        mathematical formulas shown in FIG. 15, “w” may be 1 at all        times. In this case, only the value between the lower limit        value Lv and upper limit value Uv shown in FIG. 11 is corrected,        and other values are not corrected (similar correction is also        in the case of the a axis and b axis of the vab color space). In        this variation example, the area between the lower limit value        Lv and upper limit value Uv is an example of “a surrounding        area”.    -   (6) Each pixel in object image data may be specified by a value        in a color space of an orthogonal coordinate system (for        example, first orthogonal coordinate system, second orthogonal        coordinate system, and the like), and alternately, may be        specified by a value in a polar coordinate system. In the former        case, a first polar coordinate value and second polar coordinate        value may be determined after the value of each pixel in the        object image data has been transformed into the value in the        color space of the polar coordinate system. Further, in the        latter case, the correction may be performed after the value of        each pixel in the object image data has been transformed into        the value in the color space of the first orthogonal coordinate        system.    -   (7) Further, “a surrounding area” having a shape other than        those described above may be used alternately. For example, the        surrounding area may be an area that includes “a first        orthogonal coordinate value (a first representative value)” and        does not include “a second orthogonal coordinate value (a second        representative value)”.    -   (8) Further, “a first orthogonal coordinate value (a first        representative value)” and “a second orthogonal coordinate value        (a second representative value)” may not be an average, and may        be a central value.    -   (9) The correction program 36 illustrated by FIG. 1 may or may        not be installed in the multi-function device 10 at a stage of        shipping the multi-function device 10. In the latter case, the        correction program 36 may be downloaded into the multi-function        device 10 from the Internet or may be installed in the        multi-function device 10 from a computer readable medium. A        computer readable medium that stores the correction program 36        is also a useful feature.

What is claimed is:
 1. An image processing device for creating correctedimage data by correcting object image data utilizing base image data,the image processing device comprising: a first determination unitconfigured to determine a first representative value which representsfirst pixels in the object image data, each of the first pixels having ahue within a particular range of hue; a second determination unitconfigured to determine a second representative value which representssecond pixels in the base image data, each of the second pixels having ahue within the particular range of hue; and a correction unit configuredto create the corrected image data by correcting the object image datasuch that a value of each particular pixel in the object image dataapproaches the second representative value, wherein the each particularpixel is included in a surrounding area of the first representativevalue, wherein the correction unit is configured to correct a value of acorrection target pixel such that a correction amount becomes greater asa sum of a first distance and a second distance becomes smaller, thefirst distance being a distance between the first representative valueand the value of the correction target pixel, and the second distancebeing a distance between the second representative value and the valueof the correction target pixel.
 2. The image processing device as inclaim 1, wherein the surrounding area is a particular area includingboth of the first representative value and the second representativevalue.
 3. The image processing device as in claim 2, wherein theparticular area is an area in an ellipsoid having the firstrepresentative value and the second representative value as two focuses.4. The image processing device as in claim 1, wherein a size of thesurrounding area determined in a case of the particular range being afirst range is different from a size of the surrounding area determinedin a case of the particular range being a second range which isdifferent from the first range.
 5. The image processing device as inclaim 1, wherein the first representative value is a coordinate value ina color space of an orthogonal coordinate system, the secondrepresentative value is a coordinate value in the color space of theorthogonal coordinate system, in a case where a coordinate value in thecolor space of the orthogonal coordinate system of a correction targetpixel is identical to the first representative value, the correctionunit is configured to correct the coordinate value in the color space ofthe orthogonal coordinate system of the correction target pixel into afirst coordinate value that is between the first representative valueand the second representative value, and in a case where the coordinatevalue in the color space of the orthogonal coordinate system of thecorrection target pixel is a second coordinate value that is between thefirst representative value and the second representative value, thecorrection unit is configured to correct the coordinate value in thecolor space of the orthogonal coordinate system of the correction targetpixel into a third coordinate value that is between the first coordinatevalue and the second representative value.
 6. The image processingdevice as in claim 1, wherein the first determination unit is configuredto determine the first representative value by calculating an averagevalue of a value of each pixel included in the first pixels, and thesecond determination unit is configured to determine the secondrepresentative value by calculating an average value of a value of eachpixel included in the second pixels.
 7. A non-transitory computerreadable medium including a computer program for creating correctedimage data by correcting object image data utilizing base image data,the computer program including instructions for ordering a computer toperform: determining a first representative value which represents firstpixels in the object image data, each of the first pixels having a huewithin a particular range of hue; determining a second representativevalue which represents second pixels in the base image data, each of thesecond pixels having a hue within the particular range of hue; andcreating the corrected image data by correcting the object image datasuch that a value of each particular pixel in the object image dataapproaches the second representative value, wherein the each particularpixel is included in a surrounding area of the first representativevalue, wherein the creating the corrected image data from the objectimage data includes correcting a value of a correction target pixel suchthat a correction amount becomes greater as a sum of a first distanceand a second distance becomes smaller, the first distance being adistance between the first representative value and the value of thecorrection target pixel, and the second distance being a distancebetween the second representative value and the value of the correctiontarget pixel.